Weaving Together My Life Story In A Series Of Blogs

The blog has always been a great platform for storytelling. Over time, I have shared many things about my history, my present, and goals for the future - they are:

My Life Story - Have you ever thought about the impact of big decisions that you made in your life? How about the small ones or the ones made for you? How would have your life turned out if things went the other way?

My Love Story - Michelle and met on October 15, 2010 in a serendipitous way. We were both part of a charity in Raleigh, NC and one night at a dinner struck up a conversation about many things - including our love of travel.

My Housing Story - After 13 moves and stops in three Canadian Provinces and three U.S. States, here is my journey in pictures from the Northwest to the Southeast of North America.

My Car Story - I have the dubious honor of getting speeding tickets on all 6 driveable continents - lucky that there are no cars in Antarctica! Did I ever tell you the time I passed the Polizia in Italy with my mom?

My Travel Story - The story started with a "Rollerblades and Red Bull" journey to 100 countries. It is now expanding in every direction after hitting 7 continents and the 7 wonders of the world (most with kids in tow).

My Nautical Story - I am pretty sure the love of water started in 1972 when I was six weeks old and my grandparents Bob and Dona McBain retired to Shuswap, British Columbia, Canada, and built a log cabin.

My Crazy MBA Story - In the summer of 2017, while climbing Machu Picchu, Peru as part of my wife Michelle’s International MBA from Manhattan College, I thought – why not me?

My Hockey Story - As long as I can remember, I have been playing hockey. Over four and a half decades and thousands of games later, I still lace them up a couple times a week, year-round.

My Cycling Story - When the Covid-19 pandemic first took hold in March 2020 we responded quickly as a family - including strict stay at home orders and no outside contact until we could get a handle on the risks. My attention now turned to exercise - and biking across North America (virtually).

My Retirement Story - I have no interest in disconnecting fully from the work that makes me so fulfilled. I could never see myself  in bingo-playing retired life. I want to stay curious, engaged, and adding value past the (very specific) date in 2034 that I am aiming for.

My Christmas Story - Whether traveling to see family, or going to Disney or Hawaii, or simply staying home - the season is packed with memories of family and friends.

My Music Story - My favorite music can be best defined as sad / emotional / multi-level slow music. Oddly, it is opposite of my worldview - which is normally overly-positive and optimistic.

My Movie Story - Oddly enough, I think Pretty Woman made me very interested in business. I named my cat Austin Powers - oh, and yes, "Danger" is his middle name. Our current dog is named August Rush (Auggie Doggy). Movies such as Planes, Trains & Automobiles, Forrest Gump, & National Lampoon's Vacation have become soundtracks to my life.

These are all my personal stories. My business stories wrap around channels, partnerships, alliances, and ecosystems and can be found here. 

Also business related, on this platform I named the top 100 most visible channel influencers and top 100 global women in technology groups that continue to get thousands of visitors per month.

Thanks for taking a walk with me through memory lane!

 

The "Not In My Backyard" (NIMBY) backlash against data centers has turned the physical infrastructure of artificial intelligence into a fierce national land-use battle.

For years, the tech industry marketed the cloud as an invisible metaphor, but AI’s massive appetite for resources has broken that illusion, forcing industrial-scale installations into local communities.

Currently, the United States houses over 4,500 active data centers. To fuel the AI boom, an additional 700 to 1,500 facilities are actively under construction or in development nationwide.

Check if one is in your backyard here: https://www.businessinsider.com/ai-data-center-near-me-location-tracker-2026-6

This unprecedented footprint has triggered a severe public backlash; a recent Gallup poll revealed that 71% of U.S. adults now oppose data center construction in their local areas.

Opponents cite strained local power grids, skyrocketing utility bills, immense water consumption for cooling, and disruptive noise pollution. Local rejections and high-profile lawsuits, such as the citizen-led suit against the 9-gigawatt Stratos project in Utah, are stalling major rollouts.

Moving forward, the trend will shift from urban mega-hubs like Northern Virginia to rural regions, with 67% of planned facilities targeting rural areas to avoid localized resistance.

To survive, developers must pivot to energy-aware hybrid infrastructure, secure independent power via natural gas or small nuclear reactors, and engage transparently with communities to balance national compute demands with local quality of life.

We are entering an unprecedented cycle of IPOs where we will have three companies all join the trillion (or likely multi-trillion) dollar club on day one.

SpaceX (which has xAI, X, and Grok inside), Anthropic, and OpenAI have taken the "unicorn" story to the next level.

(A unicorn is startup company valued at $1 billion or more by private investors before going public.)

The story doesn't track for all unicorns though. As of March of 2026, there are 1,356 total unicorns across all industries with 485 of them being in enterprise technology.

Fun facts:

1. The San Francisco "Bay Area" alone has 171 enterprise tech unicorns — more than 1 in 3 globally.

2. The US 🇺🇸 isn't just winning — it's lapping everyone 341 of 485 (70%) are American. The next four — China 🇨🇳 (26), UK 🇬🇧 (17), Israel 🇮🇱 (16), France 🇫🇷 (13) — combined still don't match New York City alone (42).

3. $4 trillion+ in combined value, but wildly top-heavy. The median valuation is just $1.7B — barely over the threshold. The three companies above represent 2/3 of value.

4. 2021 was a unicorn factory 148 of 485 companies — 30.5% — earned unicorn status in 2021 alone. That's more than the entire decade of 2013–2020 combined.

5. 99 companies are worth exactly $1 billion. One in five enterprise tech unicorns is sitting right at the minimum threshold, suggesting many are valued at the round number they were last marked at, not true market price discovery.

6. Sequoia Capital's portfolio is worth more than most countries' GDPs. Sequoia appears in 52 companies with a combined valuation of $2 trillion+ — mostly because of OpenAI and Anthropic.

7. The big three investors are everywhere Andreessen Horowitz (41 companies), Sequoia Capital (39), and Accel (38) appear in roughly 1 of every 12 companies on the list each.

8. The average time since joining the list is 4.1 years. The oldest — Mu Sigma Inc. (data analytics, India) — has been a unicorn for over 13 years with no exit. Lookout and Magic Leap have been waiting 12+ years.

9. Israel punches well above its weight with 16 companies, $29.4B in combined value, from a country of 10 million people. That's more unicorns per capita than any nation except the US, and ahead of Germany and Australia despite being a fraction of their size.

10. The AI wave is just getting started 88 companies joined in 2025 to March 2026 alone. Nearly 1 in 5 of the entire list in just 18 months. The newest entrants (WorkOS, AMI Labs, Nexthop AI, Axiom Math) all hit unicorn status in March 2026, and 4 of the top 8 by valuation are pure-play AI companies.

Outside of the consumer interest in big IPOs (and soon to be richest people in the world), the business interest for channel partnerships is seeing which categories of enterprise tech are getting the attention of serious money.

Early market success regularly results in huge channel plays downstream - just look at Anthropic's $100 million investment in partners a few weeks ago!

Today marks a historical moment in the rapid growth of AI.

AI agents crawling the web have, for the first time, surpassed humans in internet activity, according to Cloudflare.

The split is currently 57% machine to 43% human, at least as measured by HTTP requests. The gulf is certain to widen.

What are the bots doing, you ask?

Checking prices, comparing flights, ordering products, facilitating payments, reading pages, and, not unlike their web crawling forebears, scraping and indexing content. (But this time, for AI models not search engines)

Generative AI has spent years learning from the web, now agents are spending their time "doing" on the web. That translates to lots of page load requests, even if it’s not much time spent compared to humans.

Coming up on June 12th, we are going to have the world's first trillionaire (Elon Musk) after the SpaceX IPO

Coming up on June 12th, we are going to have the world's first trillionaire (Elon Musk) after the SpaceX IPO.

Looking at previous milestones, Bill Gates was the first to $100 billion ($185 billion in today's dollars) in April of 1999. Jeff Bezos was the first to $200 billion in August 2020.

Obviously, the first argument is how much money should sit with individuals as Elon would effectively join the G20 - the richest 20 countries of the world (in GDP) as an individual.

More interesting for me is the story (or system) behind this and what it means for the rest of the partner ecosystem.

How do more billionaires get formed in orbiting this opportunity? (excuse the space pun). How do more multi-millionaires form by rotating around each of these billionaires? And so on...

It is easy to dismiss Elon as a jerk - both in the Steve Jobs sense of management as well as many people's opinions on his foray into politics (DOGE) and acquiring Twitter.

As an analyst, in contrast, we think about algorithms, systems, methods, processes, workflows, and repeatable logic.

The Algorithm was the first book written by someone in Elon's close circle - the ex-President of Tesla about the "system" behind the story. What do we learn from this system? (both the good and the bad). How do we explain it to others?

Author Jon McNeill had already founded and sold six startups when Sheryl Sandberg introduced him to Elon Musk, who was looking for help at Tesla. McNeill was steeped in the lean principles that had made Toyota Motor Corporation a global powerhouse—principles focused on achieving efficiency and optimization by incrementally improving existing systems and processes.

What he learned from Elon at Tesla was its antithesis, an approach that required radical rethinking to explode the status quo, attack complexity, and set seemingly unrealistic goals.

--> Elon called this five-step framework “The Algorithm.”

Fun fact for our channel: - McNeil is also one of early funders/advisors of Cork alongside Datto legend Austin McChord and CEO Dan Candee. Cork is redefining how MSPs approach cybersecurity and financial promises to clients. I am also proud to be a company/board advisor.

Anyway, I don't normally recommend books - but grab this one and read it before June 12th to give you more context behind the scenes:

https://www.amazon.com/Algorithm-Hypergrowth-Formula-Transformed-Lululemon/dp/B0FFG5KFSK

AI won't replace you (in the short term). It will, however, be responsible for getting you fired

Calls being recorded for “quality purposes”, key loggers, mouse tracking, email metrics, badge monitoring, login attempts.

All surreptitious tactics to correlate and report on the productivity of employees.

What is missing? Context.

Every job is different. Top performers may execute their jobs differently when full context is understood.

—> So, what systems have almost perfected context in the past 12 months? LLMs.

That AI agent that sits on your shoulder, reads every email, watches every keystroke, listens to every conversation, analyzes every spreadsheet, records everyone else’s opinion of you.

That “copilot” that has been promised to make you superhuman is also reporting back (to who paid for that agent) the exact contribution you are making to the business.

How many hours are you actually working?

What measurable output comes from those hours?

How many meetings are you attending and not leading, taking away actions, and/or actually completing them?

How many jobs are not directly impacting revenue growth, cost control, client satisfaction, partner enablement, or product innovation?

Layoffs are hitting by the tens of thousands per company almost daily. These aren’t people who managers deem “low performers” (which was always subjective and unfair) but who AI reports aren’t providing an acceptable ROI (with surprising accuracy and getting better each day).

Case in point (in past 48 hours):

The situation at Meta has escalated rapidly as the company prepares for a massive workforce reduction (10% or 8,000 people) while simultaneously rolling out new, highly invasive tracking software.

The convergence of surveillance, imminent job cuts, and labor organizing marks a significant turning point in the technology industry internal culture (and soon to be all industries).

—> This isn’t AI replacing your job. It is AI causing your job loss.

One of the interesting (and unpredictable) side effects of AI may be the resurgence of unionized workforces.

Recruitment flyers are already in bathrooms in the U.S. 🇺🇸 and UK 🇬🇧 at Meta.

Unions first exploded on an international scale between 1877 and 1886 during the Great Upheaval. 

A second, even larger explosion occurred in the Great Depression (and the New Deal in the U.S.) during the 1930s.

Will AI trigger a massive third wave?

Demographics are a fascinating way of predicting future trends, opportunities, and threats.For example, we knew for the last 8 years that millennials would become the #1 buyer in the $6.07 trillion tech industry last year.

--> We also know that China will drop their population in half (1.41 billion to 633 million) by 2100 due to the disastrous One-Child Policy.

The average age is already 40.6 years old and they will be facing a similar economic fate as Japan because of demographics.

We are not only in the AI-race for the next 20 years, we are also in an immigration race as most of the world (minus Africa and some of the Middle East) are not at a 2.1 child replacement rate.

Far from it.

It would also behoove us (as global citizens) to encourage the economic and geopolitical development of Africa to handle the coming population boom.

--> Africa will double in population size by 2050 and the situation is a study in extreme contrast.

The continent is home to the world's most significant demographic "boom" while simultaneously navigating localized crises of violence, disease, and structural instability.

Africa’s population is "chronically young". Half of the citizens in sub-Saharan Africa are under 21 years old. About 12 million enter the labor market every year and only 3 million formal wage jobs are created annually.

This youth bulge can be a demographic dividend if educated and employed, but without opportunities, it becomes a primary driver of social instability and recruitment for extremist groups.

More challenges:

1. Over half of the low-income countries in the region are at high risk of debt distress, leaving little money for "human capital" (schools and hospitals).

2. Traditional foreign aid from the West (ie. USAID has cut upwards of 60% in past year) and China has been falling since the pandemic, forcing African nations to look toward "self-reliance" and diaspora philanthropy.

3. Severe droughts in East Africa have worsened food security, making children even more vulnerable to diseases they might otherwise survive.

--> Analysts tend to call demographics "destiny in slow motion."

Unlike economic forecasts or polling (which can shift with every tweet) demographic data is remarkably "sticky" because the people who will make up the workforce, the taxpayer base, and the buyer markets twenty years from now have, for the most part, already been born.

Marking this important moment in history on my blog.Last week we had humanoid robots setting new marathon records (power/speed) and beating professional ping pong players (dexterity/reflex).

We have noted important AI moments in the past like IBM defeating a chess grandmaster in 1997, Google defeating Go in 2015, and OpenAI passing the Turing Test in 2025.

Well, today we have 20 different AI models (LLMs) that are smarter than the average human (ranked as 100 on this chart).

More importantly, we now have 11 models running beyond genius level (130 on this chart) according to Mensa Norway.

Futurist Ray Kurzweil predicted that by 2029, computers will achieve human-level intelligence, passing a valid Turing test (already happened last year), marking the arrival of Artificial General Intelligence (AGI).

He expects this AI to possess human-level cognitive capabilities, leading to significant human-machine integration and the start of a technological/biological singularity by 2045.

He may have been too conservative on this forecast by at least 15 years.

A humanoid robot just surpassed the human world record (in half-marathon) by 7 minutes.Having worked through 32 years (and living through 53 years) of the technology industry's growth, it always amazes me what it feels like "in the moment" versus the way we wax poetic about it 20 years later.

We all know those moments.

When a young Bill Gates thought every home will have a PC, and the IBM people in the same meeting felt like the total opportunity was 10,000 hobbyists.

When a young Jeff Bezos wondered what to do with tech infrastructure they had to build for their busiest day (but sits idle for the rest of the year), and magazines start making fun of him on the front cover.

Here is the deal.

The first PC was VERY limited. But it did have slots, bays, and ports that enabled innovators and entrepreneurs to build trillions of dollars of value in the future (Thanks Steve Wozniak for winning this battle over the other Steve).

The first iPhone was VERY limited. But it did have a future vision of what an App Store could do and unleashed millions of inventive minds into the ecosystem.

The same criticism came of early LLMs.

They hallucinate. They can't be trusted. It is a consumer noveltly.

Then an LLM passed the legal bar exam (bottom 10%). Six months later it passed (top 10% - Harvard/Yale level). And then 6 months later it was writing, grading, and proctoring the exam for humans.

All in 12 months.

We have difficulty in projecting what fast growing technologies will become. Bill Gates said in 1996: "We overestimate the first 2 years, and underestimate the first 10".

So here we are with humanoid robots. They are clunky, they look funny, their fingers don't have the dexterity.

Until they do.

--> Last year, humanoid robots finished the half-marathon for the first time. The result? Twice as long as the human winner. It was funny to watch.

--> This year? They set a new world record by a staggering 7 minutes.

--> Next year? They will probably finish a half marathon in 5 to 10 minutes total - the race will have to move to a Formula One track to facilitate.

Future:

The world has been built (for thousands of years) by humans FOR humans. The idea that we need hundreds of "smart devices" in our lives may turn into the fact that we only need a very few - shaped as humans and as capable (both physically & intellectually).

Examples:

- Your smart vacuum (sorry iRobot on bankruptcy), doesn't need to be smart when a humanoid robot can use a dust pan and mop.

- Your fancy smart cat litter box can go back to the normal one when a humanoid robot cleans it after every use.

- Your car with tens of thousands of dollars in sensors, cameras, and Lidar can go back to normal when a humanoid robot can take the wheel (with safety already 10X distracted humans).

- Even as simple as your smart thermostat. It can go back to the old dial when humanoid robots (which will be in every home within 20 years) can turn the dial with much better accuracy.